Question

Regression Assignment #7 ustractions: Read the following scenarie, and thew answer the guestiones that follow (sS pints There is a well-known coerelation between temperature and crime. The hoter it gets, the more crime there is! Your local police precinct wants your help to prodict how basy they will be on a future date given the Sorecast. Using tempcrature (X) and number of crimes (Y), fill in the blank spaces in following chart to assess this temperature / crime relationship: (Note: Complete this chart offline on your own. Then, answer questions 1 through 5 by selocting the correct answer in Canvas) 1. Which of the Sollowing is correct? A). The prodictor (independent) variable is temperaturec. The critcrion (dependent) variable is crime. And the rescarchers are trying to see if crime predicts semperature B). The prodictor (independent) variable is crime. The critorion (dependent) variabk is tempcrature. And the rescarchers are trying to sec if temperature predicts crime C). The predictor (independent) variable is crime. The criterion (dependent) variable is temperature. And the rescarchers are trying to see if crime predicts temperature D). The predictor (independent) variable is temperature. The criterion (dependent) variable is crime. And the researchers are trying to see if temperature predicts crime 2. What is the oorrect repression weight (b (round to three decimall places here). (1 point) A). 3.808 B). 9.920 C).0.022 D). 60062 251 E). 775 3. Whar is the coerect regression imercept (a) (round to three decimal places here) ( 1 point) A). 310 B). 9.920 C). 0022 DI. 60062 251 E). 775 What is the correct regression equation(Y') (use three decimal places here) (1 point) A) Y 0.022x 775 B) Y 3.10X + 0.022 CY-0022x+60062 251 D), Y' “ 0.022X + 3 10 E X-0022Y+3.10 5. The local weather forecaster predicts that the temperature will be 82 degrees on Saturday. Given your regression equation, the police can expect about how many crimes that day? (For your final answer, please round to two decimal places) (I point) A). 2.33 crmes B). 2.87 crimes C).4.90 crimes D).S61 eimes E). 7.82 crimes

Answer 1

Here I attach the R code :

x=c(55,60,65,70,75,80,85,90,95,100)
y=c(2,4,4,5,7,6,8,6,4,2)
m=lm(y~x)
summary(m)

call: 1 (formula = y ~ x) Residuals: 3.2909 -1.0182 -0.0273 1.0909 3.0364 Coefficients (Intercept) 3.10909 3. 61030 0. 861 1 Min 1Q Median 3Q Max 5 6 2 Estimate Std. Error t value Pr(>lt| 0.414 0.02182 0.04580 0.476 0. 647 5 Residual standard error 2.08 on 8 degrees of freedom Multiple R-squared: 0.02758, Adjusted R-squared: 0.09397 3 F-statistic: 0. 2269 on 1 and 8 DF, p-value: 0. 6466

1. The predicted variable is crime the dependent variable is temperature. the researchers are find trying to see if temperature predicts crime. option B.

2. The correct regression weight b is 0.02182 approximately 0.022. option C.

3.The intercept is 3.10909 approximately 3.10. option A.

4. The regression equation is :

Y= 0.022 X+3.10

option D.

5. When the temperature is 82 degrees then the predicted crime is 0.022 *82 + 3 .10 = 4.904. option C